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SamIam is a comprehensive tool for modeling and reasoning with Bayesian networks, developed in Java by the Automated Reasoning Group of Professor Adnan Darwiche at UCLA. Samiam includes two main components: a graphical user interface and a reasoning engine.
Get Started with SamIam. Welcome to the SamIam program! If you are a new user of SamIam, and you have access to a Windows computer, the first thing we recommend you to do is to view the introductory video tutorial (WMV / MP4) - it gives a basic introduction to the program, including: the differences between Edit Mode and Query Mode, how to ...
SamIam is a comprehensive tool for modeling and reasoning with Bayesian networks, developed in Java by the Automated Reasoning Group of Professor Adnan Darwiche at UCLA. Samiam includes two main components: a graphical user interface and a reasoning engine.
The invocation script provided with SamIam (samiam.bat on Windows, runsamiam on Solaris) helps you do that. By default, the script gives the JVM 512 Megabytes. If your system has more memory, we recommend you increase the maximum memory limit in the script to the amount of physical RAM present on your system.
SamIam's Sensitivity Analysis is a powerful, user-friendly tool for analyzing the complex dependencies between variables in a Bayesian network and providing guidance to the belief engineer as he tweaks network parameter values.
Shows how to use the SamIam EM Learning tool to learn CPT parameters from a Hugin format case file, and how to generate simulated case file data. Tutorial: Genie Files ( WMV 21.8 MB / MPEG4 10.5 MB)
SamIam supports opening files in six popular formats for defining Bayesian networks: the Hugin .net format (v5.7 and v6.*), the Genie .dsl and .xdsl formats, the Interchange .dsc format used by the Microsoft Bayesian Network Toolkit, the Netica .dne format and the Ergo .erg format.
SamIam’s MAP tool provides an interface with which to define and calculate an answer to a Maximum a Posteriori query. Invoke the tool by selecting its menu item from the "Tools" menu or by clicking the "MAP" button on the tool bar.
In SamIam, the user can accomplish this by editing a table of conditional probability values for each variable in the belief network. The probability-editing table for a node is part of the node properties dialog window.
Expectation Maximization (EM) Learning. SamIam's EM Learning tool allows the belief engineer to learn the conditional probabilities in a network based on data, using the Expectation Maximization (EM) algorithm .
